Uptake of cervical cancer screening and its predictors among women of reproductive age in Gomma district, South West Ethiopia: a community-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is a public health challenge despite the available free screening service in Ethiopia. Early screening for cervical cancer significantly improves the chances of successful treatment of pre-cancers and cancers among women of reproductive age. Therefore, this study aimed to assess the uptake of screening and identify the factors among women of reproductive age. METHODS: A community-based cross-sectional study was conducted in Gomma Woreda, Jimma Zone, Ethiopia, from 1st to the 30th of August, 2019. The total sample size was 422. A systematic random sampling technique was employed. Data were collected using a structured questionnaire, entered in epidata, and exported and analyzed using SPSS version 20.0 software packages. Descriptive, bivariate and multivariable logistic regression analyses with 95% CI for odds ratio (OR) were performed to declare a significant predictors. RESULT: A total of 382 study participants were involved with a response rate of 90.5%. The mean age of the study participants was 26.45 ± 4.76 SD. One hundred forty-eight (38.7%) of participants had been screened for CC. Marital status (AOR = 10.74, 95%, CI = 5.02-22.96), residence (AOR = 4.45, 95%, CI = 2.85-6.96), educational status (AOR = 1.95, 95% CI = 1.12-3.49), government employee (AOR = 2.61, 95%, CI = 1.33-5.15), birth experience (AOR = 8.92, 95% CI = 4.28-19.19), giving birth at health center and government hospitals (AOR = 10.31, 95% CI = 4.99-21.62; AOR = 5.54, 95% CI = 2.25-13.61); distance from health facility (AOR = 4.41, 95% CI = 2.53-9.41), health workers encouragement (AOR = 3.23, 95% CI = 1.57-6.63), awareness on cervical cancer (AOR = 0.37, 95% CI = 0.19-0.72), awareness about CC screening (AOR = 4.52, 95%, CI = 2.71-7.55) and number of health facility visit per year (AOR = 3.63, 95%, CI = 1.86-6.93) were the predictors for the uptake of cervical cancer screening. CONCLUSION: The uptake of cervical cancer screening was low. Marital status, residence, occupation, perceived distance from screening health facility, health workers encouragement, number of health facility visits, birth experience, place of birth, and knowledge about cervical cancer screening were the predictors. There is a need to conduct further studies on continuous social and behavioral change communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".